LF images are captured from the same scene with different angles. As shown in Fig. REF , due to the occlusions and specularities, the details absent in one SAI may appear in another one, and such complementary information is helpful to predict the high frequency details of SR images. In order to capture distinctive information from various view images, we first extract shallow feature in each SAI with the sharing parameters (See Sec. III-B). Then two attention branches are designed to learn informative details from various views and channels by a sequence of stacked views and channel attention blocks (see Sec. III-C). To improve information flow across low-level and high-level layers within the two branches, dense connections [1]}, [2]}, [3]} are used. The output of these two branches are fed into a fusion module and an upscale module to generate SR image of each SAI (see Sec. III-D).
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